Single Image

Using the data from one image for one output image.


The Generator Network of an Architecture.


Module itself that comprises of other modules (layers) that perform operations on data.


A combination of Networks with specific purposes. E.g., a GAN network would consist of a Generator (G) Network as well as a Discriminator (D) Network.

Generator (G) Network

Transformed input data to new output data based on the Networks layers.

Discriminator (D) Network

Essentially tries to tell if a Networks output is fake/bad. Think of it as a human quickly comparing the G Network’s output to the original GT image to see if it’s a good result.

This network would only be used for Training purposes, and generally wouldn’t be used by VSGAN.

Super-Resolution (SR)

Result of a model with a > 1x scale output. Aka, Upscaling, Upconverting, Resizing.

Generative Adversarial Network (GAN)

Adversarial which a Generator (G) network generates data, and a Discriminator (D) tries to detect if the generated image is perceived as fake.

Low-Resolution (LR)

The low-resolution input image/data. The data you wish to transform with the model.

Ground Truth (GT) or High-Resolution (HR)

The original high resolution image/data. This data would be used for your Discriminator while training, or for comparison.